Social media sharing sites like Flickr®, Delicious or YouTube® allow millions of users to share and annotate images, documents and videos. Tagging refers to the behavior of annotating content (images, documents, web pages, etc.) with tags which are often free text keywords. In recent years, social tagging is becoming more and more popular in Web 2.0 applications where users can freely annotate Web pages, academic publications and multimedia objects. Tag recommendation is concerned with suggesting relevant tags to the users, which they could potentially use to annotate the resources they visited. Tag recommendation is beneficial for users because it can improve the user experience in their tagging process, and for the system because it expands the set of tags annotating a content object thus enriching the system.
The wealth of annotated and tagged objects on the social media sharing sites can form a solid base for reliable tag recommendation. A tag recommendation engine can benefit from collective social knowledge to provide relevant suggestions.
Problems occur when many and diverse users are subjectively social tagging documents solely based upon personal standards or interests so that the tags appear in a free-form reflecting an individual users' choice.
A simple strategy of tag recommending would seem to exploit the popular tags that are frequently used by other users to annotate an image, while the recommended tags are the intersection of this user's tag vocabulary and all the tags annotated. Such a strategy exploits collaborative knowledge and does not require the content of documents. Unfortunately, such a strategy works poorly in practice. For example, the popularity distribution of tags in a social tagging system like Flickr follows the power law, with about 50% of tags used only once and a large majority of images are only tagged by one or two users. There is a need to explore the interrelation of the objects as well as the tags annotating them. On the other hand, different users may have very different preferences on the tags they would select to tag an image. Therefore, it is also desirable to develop personalized recommendation engine for social tagging.
There is a need for a system which can result in more accurate tagging, and thus improve the user tagging experience.
There is a need for a methods and systems to explore the interrelation of the content objects as well as the tag annotating them that may recognize that different users may have very different preferences on the tags that they would select to tag a content object. Thus, the desirable system will be able to develop personalized selected recommendation for social tagging per user.